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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19312 |
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| _version_ | 1866914196502872064 |
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| author | Wu, Minghui Gao, Zhen |
| author_facet | Wu, Minghui Gao, Zhen |
| contents | This paper investigates multimodal semantic non-orthogonal transmission and fusion in hybrid analog-digital massive multiple-input multiple-output (MIMO). A Transformer-based cross-modal source-channel semantic-aware network (CSC-SA-Net) framework is conceived, where channel state information (CSI) reference signal (RS), feedback, analog-beamforming/combining, and baseband semantic processing are data-driven end-to-end (E2E) optimized at the base station (BS) and user equipments (UEs). CSC-SA-Net comprises five sub-networks: BS-side CSI-RS network (BS-CSIRS-Net), UE-side channel semantic-aware network (UE-CSANet), BS-CSANet, UE-side multimodal semantic fusion network (UE-MSFNet), and BS-MSFNet. Specifically, we firstly E2E train BS-CSIRS-Net, UE-CSANet, and BS-CSANet to jointly design CSI-RS, feedback, analog-beamforming/combining with maximum {\emph{physical-layer's}} spectral-efficiency. Meanwhile, we E2E train UE-MSFNet and BS-MSFNet for optimizing {\emph{application-layer's}} source semantic downstream tasks. On these pre-trained models, we further integrate application-layer semantic processing with physical-layer tasks to E2E train five subnetworks. Extensive simulations show that the proposed CSC-SA-Net outperforms traditional separated designs, revealing the advantage of cross-modal channel-source semantic fusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion Wu, Minghui Gao, Zhen Signal Processing Artificial Intelligence Information Theory Machine Learning This paper investigates multimodal semantic non-orthogonal transmission and fusion in hybrid analog-digital massive multiple-input multiple-output (MIMO). A Transformer-based cross-modal source-channel semantic-aware network (CSC-SA-Net) framework is conceived, where channel state information (CSI) reference signal (RS), feedback, analog-beamforming/combining, and baseband semantic processing are data-driven end-to-end (E2E) optimized at the base station (BS) and user equipments (UEs). CSC-SA-Net comprises five sub-networks: BS-side CSI-RS network (BS-CSIRS-Net), UE-side channel semantic-aware network (UE-CSANet), BS-CSANet, UE-side multimodal semantic fusion network (UE-MSFNet), and BS-MSFNet. Specifically, we firstly E2E train BS-CSIRS-Net, UE-CSANet, and BS-CSANet to jointly design CSI-RS, feedback, analog-beamforming/combining with maximum {\emph{physical-layer's}} spectral-efficiency. Meanwhile, we E2E train UE-MSFNet and BS-MSFNet for optimizing {\emph{application-layer's}} source semantic downstream tasks. On these pre-trained models, we further integrate application-layer semantic processing with physical-layer tasks to E2E train five subnetworks. Extensive simulations show that the proposed CSC-SA-Net outperforms traditional separated designs, revealing the advantage of cross-modal channel-source semantic fusion. |
| title | E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion |
| topic | Signal Processing Artificial Intelligence Information Theory Machine Learning |
| url | https://arxiv.org/abs/2509.19312 |